1. Field of the Invention
The present disclosure relates to a DeepQA-based technology, where users are presented a new question and answer tool for investigating problems.
2. Description of Related Art
In 2007, IBM® Research took on the grand challenge of building a computer system that can perform well enough on open-domain question answering to compete with champions at the game of Jeopardy!® In 2011, the open-domain question answering system dubbed Watson® beat the two highest ranked players in a two-game Jeopardy! match. But, to what degree can the question answering (QA) technology underlying Watson, a deep question answering system called DeepQA®, which was tuned for answering Jeopardy! questions, succeed in a dramatically different and extremely specialized domain such as medicine? This disclosure describes the steps used to adapt and improve performance in this as well as other domains. In addition, whereas Jeopardy! allows only “question in, single answer out” with no explanation, the disclosure elaborates upon a vision for an evidence-based clinical decision support system, based on the DeepQA technology, that affords exploration of a broad range of hypotheses and their associated evidence, as well as uncovers missing information that can be used in mixed-initiative dialog.
Jeopardy! is a quiz show that pits three contestants against each other testing their ability to understand and answer rich natural language questions very quickly. These questions often contain complex language, ambiguities, puns, and other opaque references. For any given question, the contestants compete for the first chance to answer via a handheld buzzer.
To be successful at Jeopardy!, players must retain enormous amounts of information, must have strong language skills, must be able to understand precisely what is being asked, and must accurately determine the likelihood they know the right answer. Confidence in the answer is critical, because the first player to buzz in gets the opportunity to answer the question; however, if the player answers incorrectly, the player loses the dollar value associated with the clue. The challenges in the Jeopardy! task are: 1) Questions come from a broad domain: Jeopardy! Asks questions about hundreds of thousands of things, using rich and varied natural language expressions. 2) Players must answer questions with high precision and with accurate confidence: On average, champion players must be able to correctly answer more than 85% of the questions they buzz in for and they must be confident enough to buzz in for at least 70% percent of them. 3) Answering must be very fast: Winning players must quickly determine an accurate confidence in a correct answer and buzz in quickly enough to beat their competitors consistently to the buzz.
Over a four year period, the team at IBM developed the Watson system that competed on Jeopardy! and the underlying DeepQA question answering technology (Ferrucci, D., Brown, E., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A. A., Lally, A., Murdock, J. W., Nyberg, E., Prager, J., Schlaefer, N., Welty, C. Building Watson: An Overview of the DeepQA Project. AI Magazine, Fall 2010). Watson played many games of Jeopardy! against celebrated Jeopardy! champions and, in games televised in February 2011, won against the greatest players of all time, Ken Jennings and Brad Rutter. But, DeepQA has application well beyond Jeopardy! Contrary to some popular misconceptions, DeepQA does not map the question to a database of questions and simply look up the answer. DeepQA is a software architecture for analyzing natural language content in both questions and knowledge sources. DeepQA discovers and evaluates potential answers and gathers and scores evidence for those answers in both unstructured sources, such as natural language documents, and structured sources, such as relational databases and knowledge bases.